SoroushVahidi/scidocs huggingface dataset:
The active layer morphology of organic solar cells probed with grazing incidence scattering techniques.
(2014)

Grazing incidence X-ray scattering (GIXS) provides unique insights into the morphology of active materials and thin film layers used in organic photovoltaic devices. With grazing incidence wide angle X-ray scattering (GIWAXS) the molecular arrangement of the material is probed. GIWAXS is sensitive to the crystalline parts and allows for the determination of the crystal structure and the orientation of the crystalline regions with respect to the electrodes. With grazing incidence small angle X-ray scattering (GISAXS) the nano-scale structure inside the films is probed. As GISAXS is sensitive to length scales from nanometers to several hundred nanometers, all relevant length scales of organic solar cells are detectable. After an introduction to GISAXS and GIWAXS, selected examples for application of both techniques to active layer materials are reviewed. The particular focus is on conjugated polymers, such as poly(3-hexylthiophene) (P3HT).
Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval.
(1999)

Early neuroimaging studies often failed to obtain evidence of medial temporal lobe (MTL) activation during episodic encoding or retrieval, but a growing number of studies using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have provided such evidence. We review data from fMRI studies that converge on the conclusion that posterior MTL is associated with episodic encoding; too few fMRI studies of retrieval have reported MTL activations to allow firm conclusions about their exact locations. We then turn to a recent meta-analysis of PET studies (Lepage et al., Hippocampus 1998;8:313-322) that appears to contradict the fMRI encoding data. Based on their analysis of the rostrocaudal distribution of activations reported during episodic encoding or retrieval, Lepage et al. (1998) concluded that anterior MTL is strongly associated with episodic encoding, whereas posterior MTL is strongly associated with episodic retrieval. After considering the evidence reviewed by Lepage et al. (1998) along with additional studies, we conclude that PET studies of encoding reveal both anterior and posterior MTL activations. These observations indicate that the contradiction between fMRI and PET studies of encoding was more apparent than real. However, PET studies have reported anterior MTL encoding activations more frequently than have fMRI studies. We consider possible sources of these differences.
Dynamic simultaneous fare proration for large-scale network revenue management
(2012)

Network revenue management is concerned with managing demand for products that require inventory from one or several resources by controlling product availability and/or prices in order to maximize expected revenues subject to the available resource capacities. One can tackle this problem by decomposing it into resource-level subproblems that can be solved efficiently, e.g. by dynamic programming (DP). We propose a new dynamic fare proration method specifically having large-scale applications in mind. It decomposes the network problem by fare proration and solves the resource-level dynamic programs simultaneously using simple, endogenously obtained dynamic marginal capacity value estimates to update fare prorations over time. An extensive numerical simulation study demonstrates that the method results in tightened upper bounds on the optimal expected revenue, and that the obtained policies are very effective with regard to achieved revenues and required runtime.
IP covert timing channels: design and detection
(2004)

A network covert channel is a mechanism that can be used to leak information across a network in violation of a security policy and in a manner that can be difficult to detect. In this paper, we describe our implementation of a covert network timing channel, discuss the subtle issues that arose in its design, and present performance data for the channel. We then use our implementation as the basis for our experiments in its detection. We show that the regularity of a timing channel can be used to differentiate it from other traffic and present two methods of doing so and measures of their efficiency. We also investigate mechanisms that attackers might use to disrupt the regularity of the timing channel, and demonstrate methods of detection that are effective against them.
Education, Signaling and Mismatch
(2016)

We assess the importance education as a signal of workersskills and the e¤ects of poor signaling quality on labor market outcomes. We do so by merging a frictional labor market model with a signaling setup where there is a privately observed idiosyncratic component in the cost of education. Given that highly skilled workers cannot correctly signal their abilities, their wages will be lower and they will not be matched to the "right" vacancies, or may be unemployed. Skilled workers will then have lower incentives to move to high productivity markets. Furthermore, fewer vacancies will be created in labor markets where skills matter, and incentives for workers to invest in education will be lower. Overall, an economy where education is a noisier signal generates lower educational attainment, higher unemployment and lower productivity. In addition, we provide evidence suggesting that education plays a poor signaling role in Latin American countries. We then calibrate our model using Peruvian data, and through a quantitative exercise we show that this mechanism could be relevant to explain the relatively bad performance of labor markets in Latin American countries. 1E-mail: larozamena@utdt.edu, hru¤o@utdt.edu. We are grateful to Fernando Álvarez Parra for his comments and suggestions.
A Fuzzy Logic Controller for Autonomous Wheeled Vehicles
(2006)

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various (and sometimes simultaneous) navigational issues,including: • reaching a static target (Baturone, et al., 2004, Boada, et al., 2005, El Hajjaji & Bentalba, 2003, Maeda et al., 1991, Chen & Ozguner, 2005), • tracking moving targets (Ollero, et al., 2001), • maintaining stable vehicular velocity (Holzmann et al., 1998, Nobe & Wang, 2001), • following a lane or a wall (Rosa & Garcia-Alegre, 1990, Hessburg & Tomizuka, 1994, Peng & Tomizuka, 1993) and, • avoiding collision with static and dynamic obstacles (Baturone, et al., 2004, Murphy, 2001, Godjevac, et al., 2001, Seraji, 2005, Lee & Wang, 1994, Wheeler & Shoureshi, 1994, Ye & Wang, 2001). Several researchers combined fuzzy logic controllers with various learning techniques, such as: • supervised learning method (Godjevac, 2001), • evolutionary method (Hoffman, 2001, Kim, et al., 2001), • neural network (Pasquier, et al., 2001, Cang, et al., 2003), • reinforcement learning (Dai, et al., 2005) and, • optimization methods (Hong, 1997, Sanchez, et al., 1999).
A Case Study on User Experience (UX) Evaluation of Mobile Augmented Reality Prototypes
(2013)

Mobile Augmented Reality (MAR) blends the real world with digital objects especially in ubiquitous devices such as smartphones. The MAR applications provide an intelligent interface for users. In this, valuable digital information is advertised in physical spaces. However, the success of these applications is tied directly to the degree of user acceptance. This makes understanding the needs and expectations of the MAR’s potential users of paramount importance for designing and building the proper application. The objective of the paper is to expose an important gap in the development of novel applications in the virtual world. Previous research has shown that it is essential to study and understand the needs and expectations of the potential users of the upcoming application or system. Studying user needs and expectations before offering the developed application ensures a minimum level of acceptance and, of course, success. This paper presents a detailed study comprising of a userexperience (UX) evaluation of different prototypes through the use of three different UX evaluation methods. This kind of evaluation allows new developments to offer systems, which do not fail. The main contributions of this study are that it: 1) solicits expectations when consumers use MAR applications, 2) assesses the UX over different prototypes using three different metrics, 3) provides methodological insights on UX evaluation experiments and, 4) is useful for anyone who wants to develop handheld applications after understanding user expectations and how his experience should progress. The results of the study show that users value concreteness, realizability, personalization, novelty, intuitiveness and the usefulness of presented information. Paying attention to these factors can help develop more acceptable MAR applications and lead to more novel future designs.
A Computational Analysis of Mahabharata
(2016)

Indian epics have not been analyzed computationally to the extent that Greek epics have. In this paper, we show how interesting insights can be derived from the ancient epic Mahabharata by applying a variety of analytical techniques based on a combination of natural language processing, sentiment/emotion analysis and social network analysis methods. One of our key findings is the pattern of significant changes in the overall sentiment of the epic story across its eighteen chapters and the corresponding characterization of the primary protagonists in terms of their sentiments, emotions, centrality and leadership attributes in the epic saga.
Looking Beyond the Simple Scenarios: Combining Learners and Optimizers in 3D Temporal Tracking
(2017)

3D object temporal trackers estimate the 3D rotation and 3D translation of a rigid object by propagating the transformation from one frame to the next. To confront this task, algorithms either learn the transformation between two consecutive frames or optimize an energy function to align the object to the scene. The motivation behind our approach stems from a consideration on the nature of learners and optimizers. Throughout the evaluation of different types of objects and working conditions, we observe their complementary nature — on one hand, learners are more robust when undergoing challenging scenarios, while optimizers are prone to tracking failures due to the entrapment at local minima; on the other, optimizers can converge to a better accuracy and minimize jitter. Therefore, we propose to bridge the gap between learners and optimizers to attain a robust and accurate RGB-D temporal tracker that runs at approximately 2 ms per frame using one CPU core. Our work is highly suitable for Augmented Reality (AR), Mixed Reality (MR) and Virtual Reality (VR) applications due to its robustness, accuracy, efficiency and low latency. Aiming at stepping beyond the simple scenarios used by current systems, often constrained by having a single object in the absence of clutter, averting to touch the object to prevent close-range partial occlusion or selecting brightly colored objects to easily segment them individually, we demonstrate the capacity to handle challenging cases under clutter, partial occlusion and varying lighting conditions.
A Machine Learning-based Forensic Discriminator of Pornographic and Bikini Images
(2018)

The increased use of microcomputers and smart-phones has contributed to social progress, but it has also facilitated the exchange of illegal files, such as child pornography photographs, increasing the demand for digital forensic examination in these devices, which can store more than 300,000 images. Based on the large number of files to be analyzed, it is necessary to use capable algorithms to perform this detection, especially in the most challenging scenarios, such as the distinction between pornographic and bikini images. In this work, we present an approach that improves the ``Algorithm for detection of Nudity proposed by Ap-Apid. Our method improved the performance of this algorithm by using machine learning in extracted features from detected skin regions instead of using static rules to classify this kind of images. In addition, we used detected faces as features in order to mitigate false positives in portrait photos. For conducting the training, validation and testing phases, we used the AIIA-PID4 pornographic data set. Furthermore, we also present a statistical analysis by comparing our approach to two algorithms, one based on the “Algorithm for detection of Nudity” and another proposed by the AIIA-PID4 pornographic data set author. The experimental results showed that we achieved an accuracy of 96.96% and 94.94 in the F1-score metric, increasing the accuracy by 79.19% and 18.21% compared to the referred works, respectively.
Mobile technology habits: patterns of association among device usage, intertemporal preference, impulse control, and reward sensitivity.
(2016)

Mobile electronic devices are playing an increasingly pervasive role in our daily activities. Yet, there has been very little empirical research investigating how mobile technology habits might relate to individual differences in cognition and affect. The research presented in this paper provides evidence that heavier investment in mobile devices is correlated with a relatively weaker tendency to delay gratification (as measured by a delay discounting task) and a greater inclination toward impulsive behavior (i.e., weaker impulse control, assessed behaviorally and through self-report) but is not related to individual differences in sensitivity to reward. Analyses further demonstrated that individual variation in impulse control mediates the relationship between mobile technology usage and delay of gratification. Although based on correlational results, these findings lend some backing to concerns that increased use of portable electronic devices could have negative impacts on impulse control and the ability to appropriately valuate delayed rewards.
Model predictive control for three-level boost converter in photovoltaic systems
(2017)

In this paper, a three-level Boost (TLB) converter maximum power point tracking (MPPT) control strategy for a two-stage photovoltaic (PV) system based on model predictive control (MPC) is proposed. This method realizes fast and precise control through establishment of a prediction model and minimization of a cost function. In the work, first a three-level predictive model for the boost converter is extracted, then a predictive control algorithm using the Perturb and Observe (P&O) method is proposed, and subsequently voltage balance of the neutral point of the converter is added to the control loop. Based on the proposed method, since the inverter is not obligated to control the DC-link neutral point voltage, its modulation can be used to realize other controls on the AC side to improve grid operation. Results of the control scheme have been verified through Matlab / Simulink, and the results have been in agreement with the expectations.
Using Distributed Scrum for Supporting an Online Community-A Qualitative Descriptive Study of Students ’ Perceptions
(2016)

One purpose of higher education is to prepare students for a modern and ever-changing global society characterized by increasing complexity and collaborative environments. Scrum is an agile, widely used framework for project management dealing with the development of complex products. Scrum projects are conducted in small, empowered teams with intense communication, interaction and collaboration between the team members, facilitated by a servant-leader Scrum master. Scrum has been commonly used in professional software development and is also now being adopted in other areas, including education. There have been few studies of the application of Scrum in higher education and very few of them have studied distributed Scrum in an online context. An online learning community has several positive effects for students such as increased learning, engagement, retention and lower risks for isolation and dropouts. Participating in and contributing to a team is dependent on a sense of community, which can be difficult to build up in a distributed environment where members are geographically dispersed and do not have the possibility to meet and communicate face to face. This study examines to what extent and how distributed Scrum can support building an online learning community, from a student perspective. Twenty students, enrolled in an online course in distributed software development, participated in four Scrum projects as members of distributed Scrum teams, each team consisting of five students. Students’ perceptions were investigated by conducting semi-structured interviews. The interview transcripts were analyzed according to Rovai’s four dimensions of a classroom community. The results indicate that students were very satisfied with their distributed Scrum projects and that they experienced a high degree of flexibility during the projects. The Scrum process promoted and initiated communication and interaction among students and they learned how to communicate and collaborate effectively in an online environment. The transparency in Scrum was perceived as a key factor to open communication and effective collaboration and also contributed to increasing their motivation and engagement in the projects. Another interesting outcome of this study was understanding the importance of creating a team with members who are similar regarding competence level, ambition and preferences in working schedule.
Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: a Lyapunov-based approach
(2004)

The problem of asymptotic stabilization for straight and circular forward/backward motions of a tractor-trailer system is addressed using Lyapunov techniques. Smooth, bounded, nonlinear control laws achieving asymptotic stability along the desired path are designed, and explicit bounds on the region of attraction are provided. The problem of asymptotic controllability with bounded control is also addressed.
Bloom Filter Performance on Graphics Engines
(2011)

Bloom filters are a probabilistic technique for large-scale set membership tests. They exhibit no false negative test results but are susceptible to false positive results. They are well-suited to both large sets and large numbers of membership tests. We implement the Bloom filters present in an accelerated version of BLAST, a genome biosequence alignment application, on NVIDIA GPUs and develop an analytic performance model that helps potential users of Bloom filters to quantify the inherent tradeoffs between throughput and false positive rates.
Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
(2003)

We present attribute bagging (AB), a technique for improving the accuracy and stability of classi#er ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classi#ers are built. The induced classi#ers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classi#cation accuracy and voting using only the best subsets further improves the resulting performance of the ensemble. ? 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Your liking is my curiosity : a social popularity intervention to induce curiosity
(2018)

Our actions and decisions are regularly influenced by the social environment around us. Can social environment be leveraged to induce curiosity and facilitate subsequent learning? Across two experiments, we show that curiosity is contagious: social environment can influence people’s curiosity about the answers to scientific questions. Our findings show that people are more likely to become curious about the answers to more popular questions, which in turn influences the information they choose to reveal. Given that curiosity has been linked to better learning, these findings have important implications for education.
Android permissions: a perspective combining risks and benefits
(2012)

The phenomenal growth of the Android platform in the past few years has made it a lucrative target of malicious application (app) developers. There are numerous instances of malware apps that send premium rate SMS messages, track users' private data, or apps that, even if not characterized as malware, conduct questionable actions affecting the user's privacy or costing them money. In this paper, we investigate the feasibility of using both the permissions an app requests, the category of the app, and what permissions are requested by other apps in the same category to better inform users whether the risks of installing an app is commensurate with its expected benefit. Existing approaches consider only the risks of the permissions requested by an app and ignore both the benefits and what permissions are requested by other apps, thus having a limited effect. We propose several risk signals that and evaluate them using two datasets, one consists of 158,062 Android apps from the Android Market, and another consists of 121 malicious apps. We demonstrate the effectiveness of our proposal through extensive data analysis.
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
(2013)

The increasing popularity of Android apps makes them the target of malware authors. To defend against this severe increase of Android malwares and help users make a better evaluation of apps at install time, several approaches have been proposed. However, most of these solutions suffer from some shortcomings; computationally expensive, not general or not robust enough. In this paper, we aim to mitigate Android malware installation through providing robust and lightweight classifiers. We have conducted a thorough analysis to extract relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set. Our results show that we are able to achieve an accuracy as high as 99% and a false positive rate as low as 2.2% using KNN classifier.
Detecting Malware for Android Platform: An SVM-Based Approach
(2015)

In recent years, Android has become one of the most popular mobile operating systems because of numerous mobile applications (apps) it provides. However, the malicious Android applications (malware) downloaded from third-party markets have significantly threatened users' security and privacy, and most of them remain undetected due to the lack of efficient and accurate malware detection techniques. In this paper, we study a malware detection scheme for Android platform using an SVM-based approach, which integrates both risky permission combinations and vulnerable API calls and use them as features in the SVM algorithm. To validate the performance of the proposed approach, extensive experiments have been conducted, which show that the proposed malware detection scheme is able to identify malicious Android applications effectively and efficiently.